The goal of multiple-objective optimization, in stark contrast to the single-objective case where the global optimum is desired (except in certain multimodal cases), is to maximize or minimize multiple measures of performance simultaneously whereas maintaining a diverse set of Pareto-optimal solutions. The concept of Pareto optimality refers to the set of solutions in the feasible objective space that is nondominated. A solution is considered to be nondominated if it is no worse than another solution in all objectives and strictly better than that solution in at least one objective. Consider a situation where both ƒ1 and ƒ2 objectives are to be minimized, but where the two objectives are in conflict, at least to some extent, with each other. Because both objectives are important, there cannot be a single solution that optimizes the ƒ1 and ƒ2 objectives; rather, a set of optimal solutions exists which depict a tradeoff. Classical multiple-objective optimization techniques are advantageous if the decision maker has some a priori knowledge of the relative importance of each objective. Because classical methods reduce the multiple-objective problem to a single objective, convergence proofs exist assuming traditional techniques are employed. Despite these advantages, real-world problems, such as satellite constellation design optimization and airline network scheduling optimization, challenge the effectiveness of classical methods. When faced with a discontinuous and/or nonconvex objective space, not all Pareto-optimal solutions may be found. Additionally, the shape of the front may not be known. These methods also limit discovery in the feasible solution space by requiring the decision maker apply some sort of higher-level information before the optimization is performed. Furthermore, only one Pareto-optimal solution may be found with one run of a classical algorithm.